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Comment by jjoonathan | Hacker News Reader

I read Strang and then Axler. Strang is great at numerics but weak at presenting the abstract picture. I feel like if I had taken, say, finite elements (or any other subject where it's important to take the abstract / infinite dimensional picture seriously before reducing to finite dimensions) right after Strang without reading LADR then I'd have been seriously underprepared.

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You have a point in that to understand any particular subject well, it makes sense to read more than one book on it, at least to compare the different perspectives.

Also worth noting that Strang has a couple of similar linear algebra books, so we might not even be discussing the same text.

That's entirely possible, but in the context of introductory books I think it's fair to assume & limit scope to Strang's "Introduction to Linear Algebra" and Axler's "Linear Algebra Done Right."

I am an applications-oriented person and my inclination was to go directly from a matrix/determinant heavy picture into applications. Strang['s intro text] only. I am extremely glad that someone intercepted me and made me get some practice with abstract vector spaces, operators, and inner product spaces first, using Axler. This practice bailed me out and differentiated me from peers on a number of occasions, so I want to pass down the recommendation.

FWIW I think this is the benefit of Strang. If you're in science or engineering or statistics, often you don't need the general picture, and IMO too much generality gets in the way of understanding. Start with a good understanding of the most important cases that appear in applied work, and drill them until you're fluent with them. Then generalizing will be easier.

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